1 omotion of Breastfeeding Intervention Trial (
PROBIT).
2 emonstrate its application in a large trial (
PROBIT).
3 SV-2 status in women and men using bivariate
probit.
4 We undertook a secondary analysis of
PROBIT (
1996-2010), a birth cohort study nested within a
5 The
probit 9 standard for quarantine treatment efficacy has
6 The
probit 95% limit of detection of the assay was determine
7 In instrumental variable
probit analyses accounting for factors simultaneously as
8 Using a bioassay methodology and
probit analyses, LC(50) and LC(90) values were calculate
9 probability was 0.28 FFU/ml as determined by
probit analysis (p </= 0.05).
10 Although
probit analysis could not be performed with the availabl
11 Probit analysis determined the 95% detection level was 5
12 Probit analysis estimated sensitivity (95% detection) of
13 potential control failure was detected using
probit analysis estimates for cypermethrin, deltamethrin
14 Thresholds were determined based on
probit analysis of psychometric functions generated usin
15 Probit analysis of survival in the JNJ-26366821- and sal
16 lds (FAVL-ED50) were also determined using a
probit analysis of the dosage.
17 A
probit analysis related the duration of ductal patency t
18 Probit analysis results revealed that PS externalization
19 still protect 50% of mice was calculated by
probit analysis to be 9.4 hours.
20 e limit of detection (LOD), as determined by
probit analysis using dilutions of the 2nd HBV internati
21 (DMF) of tag-free rhMFG-E8 calculated using
probit analysis was 1.058.
22 The limit of detection calculated by
probit analysis was 23.8 copies/ml using the 2nd Interna
23 Analytical sensitivity determined by
probit analysis was between 6.2 and 9.0 IU/ml.
24 The analytical sensitivities determined by
probit analysis were 19.3 copies/ml for the 1-ml assay a
25 By
probit analysis, each additional month of PDA and hemody
26 By
probit analysis, the 95% LoD was 1 copy of HIV-1 RNA per
27 By 95%
probit analysis, the limit of detection (LoD) using the
28 c regression produces similar results to the
probit analysis.
29 ary responses and ED50s were estimated using
Probit analysis.
30 d was determined to be 7.74 HCV RNA IU/ml by
probit analysis.
31 ile ranges (IQR, 25%-75% seen) determined by
probit analysis.
32 ure threshold for damage was calculated with
probit analysis.
33 SV-2 status in women and men using bivariate
probit analysis.
34 a sets for HP and TNAI were insufficient for
probit analysis; however, there was 100% detection at >/
35 outcome were recorded and analyzed with both
probit and logistic regression analyses.
36 Mortality data from previously reported
probit and logit analyses from thousands of patients tre
37 Using both
Probit and Multivariate Probit models we found that fami
38 Bivariate ordered
probit and ordinal logistic regression models were used
39 We used fractional
probit and Poisson regression models to assess the co-pr
40 ed an inverse probability-weighted two-part,
probit,
and generalized linear model to estimate increme
41 higher wealth had a direct negative effect (
probit coefficient -0.16, 95% CI -0.25 to -0.06), which
42 e, partially mediated by positive symptoms) (
probit coefficient [beta] = 0.12; P = .002); while stabl
43 increases in the probability of tooth loss (
probit coefficients were 0.469 (95% confidence interval:
44 The bivariate
probit demonstrated significant correlation between the
45 PROBIT enrolled 17 046 infants at birth and followed the
46 The mean and SD of the
probit fitted cumulative Normal function were used to es
47 The results show that in model 1, the
probit link function is a more appropriate approach to d
48 onmental factors on the basis of the ordinal
probit model (also called threshold model) that assumes
49 A bivariate
probit model estimated the effects of risk while control
50 The
probit model indicated that outcome improved across the
51 A quasi-experimental instrumental variables
probit model of the association correlation of ECT admin
52 Comparison of
probit model results with previous results demonstrates
53 The
probit model suggested that increasing age (p=0.03), pae
54 rt approach uses a phylogenetic multivariate
probit model to accommodate binary and continuous traits
55 Using a random-effects,
probit model to compare the differences in health outcom
56 between the 2 groups, we created a bivariate
probit model to estimate the probability of repeat inter
57 two-factor theory and empirically ordered a
probit model to identify gender differences in job satis
58 er applicability, we extend the phylogenetic
probit model to incorporate categorical traits, and demo
59 First, a
probit model was used to estimate the probability of con
60 A
probit model was used, weighted by the product of the co
61 A dynamic random effects bivariate panel
probit model with initial conditions (Wooldridge-type es
62 A dynamic version of a random effects panel
probit model with initial conditions is estimated on the
63 We estimated a
probit model with state indicators to adjust for state-l
64 The proposed ordinal
probit model, combined with the composite model space fr
65 Employing the multivariate
probit model, we further highlight interdependencies in
66 oth loss by fitting an instrumental variable
probit model.
67 rger than those of the instrumental variable
probit model.
68 d year as evaluated an instrumental variable
probit model.
69 phenotype was estimated with a fixed effects
probit model.
70 arket channels through a recursive bivariate
probit model.
71 opose an inference pipeline for phylogenetic
probit models that greatly outperforms BPS.
72 Using both Probit and Multivariate
Probit models we found that familiarity with antibiotics
73 intrinsic conditional autoregressive spatial
probit models were used to determine the risk of a child
74 Multivariable ordinary least squares and
probit models were used to estimate the association betw
75 Using logit and
probit models with a threshold of 7 seeds, we found diff
76 We used an ordered
probit multivariate analysis to link evaluation scores t
77 Therefore, this study used a multivariate
probit (
MVP) approach to examine the factors influencing
78 -0.99; P = .04) and in instrumental variable
probit regression (coefficient, -0.60; 95% CI, -1.04 to
79 We analysed paired comparison data using
probit regression analysis and rescaled results to disab
80 We analysed paired comparison responses with
probit regression analysis on all 220 unique states in t
81 By applying
probit regression analysis, the analytical sensitivity w
82 e obliterating material was estimated during
probit regression analysis.
83 ex space and a linear zero-sum constraint on
probit regression coefficients.
84 A 2-part econometric model (
probit regression model and generalized linear model wit
85 Probit regression model was also used in sensitivity ana
86 A multivariable
probit regression model was used to compare proportions
87 variate' representation of the cluster, in a
probit regression model.
88 Probit regression models were developed to compare ASP-P
89 We then used linear or
probit regression to estimate the associations of the po
90 model with age-adjusted and gender-adjusted
probit regression to estimate the direct effect of socio
91 We first used
probit regression to model the associations of 2 tobacco
92 Weighted multivariable
probit regression was used to compare proportions of ind
93 EC(95)) of ropivacaine were calculated using
probit regression.
94 ivariable logistic and instrumental variable
probit regressions on data from the Multiple Risk Factor
95 We developed an ordered
probit statistical model to assess adjusted outcome as a
96 Methods considered include
probit structural equation models, 2-stage logistic mode
97 whole blood standards (validation); and ii)
PROBIT trial samples (application) in which paediatricia
98 omotion of Breastfeeding Intervention Trial (
PROBIT),
we included 13,557 participants (79.5% response
99 In
PROBIT,
we successfully quantified fasting adiponectin f